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Posts
Squashing a Multiplex into a Monoplex Using RWR_network_aggregation
Published:
It’s not impossible that you’ll at some point with to collapse down your multiplex network into a multigraph. RWRtoolkit provides two methods of multiplex network aggregation.
Exploring Gene Sets with RWR_LOE
Published:
The RWR_LOE command uses RWR to rank all genes in the multiplex network with respect to a geneset, which provides biological context for the seed genes in the gene set using the multiple lines of evidence (i.e. layers) contained within the multiplex network. The output of RWR_LOE is a matrix with random walk scores, and their associated ranks. Each row additionally contains the number of seeds within the network, the total supplied number of seeds, the network name, the modified name, and the seed gene set name. The connectivity between seed genes and the top n ranking genes can be visualized as a subnetwork in Cytoscape via the RCy3 implementation of CyREST (Gustavsen 2019, Ono 2015) by setting the cyto flag.
Multiple RWR_LOE Output Comparision for Differential Ranking
Published:
The RWR_LOE command uses RWR to rank all genes in the multiplex network with respect to a geneset, which provides biological context for the seed genes in the gene set using the multiple lines of evidence (i.e. layers) contained within the multiplex network. The output of RWR_LOE is a matrix with random walk scores, and their associated ranks. Each row additionally contains the number of seeds within the network, the total supplied number of seeds, the network name, the modified name, and the seed gene set name. The connectivity between seed genes and the top n ranking genes can be visualized as a subnetwork in Cytoscape via the RCy3 implementation of CyREST (Gustavsen 2019, Ono 2015) by setting the cyto flag. Users can use RWR_LOE output from two separate gene sets on the same multiplex to explore data driven differences between those seed sets of interest.
Seed Set Connectivity Calculations with RWR_CV
Published:
This vignette descibes the usage of RWR_CV with respect to differing gene sets and the parsing of RWR_CV’s output.
Determining Network Validity with RWR_CV
Published:
This vignette describes the usage of RWR_CV with respect to validating a multiplex network against an independently curated set of highly connected genes.
Calculating Network statistics using RWR_Netstats
Published:
With NetStats, you can generate statistics on your networks with a number of different functions, from generating basic network statistics on a paritcular network layer or multiplex to comparing individual networks against other networks or multiplexes. For example, if you have network layer acting as a gold set standard, you can use that network to test against another non-gold set network or multiplex to determine how closely related they are.
Using RWR_shortest_paths between Seed Sets to Explore Multiplex Connectivity
Published:
With Shortest Paths the shortest paths function, we generate a list of the shortest paths between two vertices ordered by the number of nodes traversed. Not only may any two vertices be supplied as potential source and target nodes, but entire sets may be supplied as well.
Building a Multiplex from scratch with RWR_Make_Multiplex
Published:
In order to use any of the features of the RWRtoolkit package, you fill first need a multiplex network. A multiplex network is a topological construction of a series of networks. Instead of collapsing all networks into a single layer, a multiplex will instead maintain each layer’s distinct topology with edges existing between the same node within each layer (i.e. Node A existing in layer 1 will have edges to node A existing in layers 2, 3, and so on).
portfolio
iRF_LOOPy
High-performance Python package for inferring Gene Regulatory Networks (GRNs) using Iterative Random Forest (iRF).
metnet-direct-auxiliary
Codebase for analyzing the link between fungal metabolomic outputs and exogenous treatment triggers.
RWRtoolkit
Command-line and R toolkit for performing Random Walk with Restart (RWR) analyses on multiplex biological networks.
Using Bipartite Networks to Determine Interactions Between Analytes and Chemical Treatments
Patent on a bipartite network-based system for ranking metabolite–treatment relationships from LC/MS data using network centrality.
publications
Eyeing the patterns: Data visualization using doubly‑seriated color heatmaps
Published in Advances in Computers, 2020
Book chapter on advanced data visualization techniques using doubly‑seriated heatmaps.
Recommended citation: Lane, M., Maiocco, A., Bhatia, S.K., Climer, S. (2020). “Eyeing the patterns: Data visualization using doubly‑seriated color heatmaps.” Advances in Computers.
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Synchronized genetic activities in Alzheimer’s brains revealed by heterogeneity‑capturing network analysis
Published in bioRxiv, 2020
Preprint using network heterogeneity analysis to reveal genetic synchrony in Alzheimer’s disease.
Recommended citation: Climer, S., Templeton, A.R., Garvin, M., Jacobson, D., Lane, M., et al. (2020). “Synchronized genetic activities in Alzheimer’s brains revealed by heterogeneity‑capturing network analysis.” bioRxiv.
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Antiviral Strategies Against SARS‑CoV‑2: A Systems Biology Approach
Published in Methods in Molecular Biology, 2022
Book chapter describing systems biology approaches to antiviral strategies against SARS-CoV-2.
Recommended citation: Prates, E. T., Garvin, M. R., Jones, P., … Lane, M. (2022). “Antiviral Strategies Against SARS‑CoV‑2: A Systems Biology Approach.” Methods in Molecular Biology.
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Lipo‑Chitooligosaccharides Induce Specialized Fungal Metabolite Profiles That Modulate Bacterial Growth
Published in mSystems, 2022
mSystems article on how lipo-chitooligosaccharides affect fungal metabolite profiles and microbial interactions.
Recommended citation: Rush, T. A., Tannous, J., Lane, M. J., … Ané, J.-M. (2022). “Lipo‑Chitooligosaccharides Induce Specialized Fungal Metabolite Profiles That Modulate Bacterial Growth.” mSystems.
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Exploring the role of plant lysin motif receptor‑like kinases in regulating plant‑microbe interactions in the bioenergy crop Populus
Published in Computational and Structural Biotechnology Journal, 2023
Journal article on plant lysin motif receptor-like kinases and their role in plant-microbe interactions in Populus.
Recommended citation: Cope, K. R., Prates, E. T., Miller, J. I., … Lane, M. (2023). “Exploring the role of plant lysin motif receptor‑like kinases in regulating plant‑microbe interactions in the bioenergy crop Populus.” Computational and Structural Biotechnology Journal.
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Genetics of varicose veins reveals polygenic architecture and genetic overlap with arterial and venous disease
Published in Nature Cardiovascular Research, 2023
Nature Cardiovascular Research article detailing the polygenic architecture of varicose veins.
Recommended citation: Levin, M. G., Huffman, J. E., Verma, A., … Lane, M. (2023). “Genetics of varicose veins reveals polygenic architecture and genetic overlap with arterial and venous disease.” Nature Cardiovascular Research.
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Identification of novel, replicable genetic risk loci for suicidal thoughts and behaviors among US military veterans
Published in JAMA Psychiatry, 2023
JAMA Psychiatry study identifying replicable genomic loci for suicidal thoughts and behaviors among 633,778 US military veterans through large-scale GWAS.
Recommended citation: Kimbrel, N. A., Ashley-Koch, A. E., Qin, X. J., Lindquist, J. H., … Lane, M. J., et al. (2023). “Identification of novel, replicable genetic risk loci for suicidal thoughts and behaviors among US military veterans.” JAMA Psychiatry, 80(2), 135–145.
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Longitudinal Effects on Plant Species Involved in Agriculture and Pandemic Emergence Undergoing Changes in Abiotic Stress
Published in ACM Conference, 2023
Conference paper studying abiotic stress impacts on agriculture and pandemic risk in plant species over time.
Recommended citation: Cashman, M., Vergara, V. G. M., Lane, M., et al. (2023). “Longitudinal Effects on Plant Species Involved in Agriculture and Pandemic Emergence Undergoing Changes in Abiotic Stress.” ACM Conference.
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A glimpse into the fungal metabolomic abyss: Novel network analysis reveals relationships between exogenous compounds and their outputs
Published in PNAS Nexus, 2023
PNAS Nexus article unveiling fungal metabolomic networks and compound relationships via network analysis.
Recommended citation: Meena, M. G., Lane, M. J., Tannous, J., et al. (2023). “A glimpse into the fungal metabolomic abyss: Novel network analysis reveals relationships between exogenous compounds and their outputs.” PNAS Nexus.
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Climate adaptation in P. trichocarpa: key adaptive loci identified for stomata and leaf traits
Published in bioRxiv, 2024
Preprint identifying genetic loci underlying climate-adaptive stomatal and leaf traits in Populus trichocarpa through GWAS and predictive modeling across over 1,300 genotypes.
Recommended citation: Klein, M. C., Meng, Z., Bailey-Bale, J., Milner, S., Shi, P., Muchero, W., … Lane, M., et al. (2024). “Climate adaptation in P. trichocarpa: key adaptive loci identified for stomata and leaf traits.” bioRxiv. https://doi.org/10.1101/2024.07.11.603099
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MENTOR: Multiplex Embedding of Networks for Team‑Based Omics Research
Published in bioRxiv, 2024
Preprint presenting MENTOR, a multiplex network embedding framework for collaborative omics studies.
Recommended citation: Sullivan, K. A., Miller, J. I., Townsend, A., … Lane, M. (2024). “MENTOR: Multiplex Embedding of Networks for Team‑Based Omics Research.” bioRxiv.
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Analyses of GWAS signal using GRIN identify additional genes contributing to suicidal behavior
Published in Communications Biology, 2024
Peer-reviewed study introducing GRIN, a method for refining GWAS results using biological networks to uncover additional genes contributing to suicidal behavior across cohorts.
Recommended citation: Sullivan, K. A., Lane, M., Cashman, M., Miller, J. I., Pavicic, M., Walker, A. M., et al. (2024). “Analyses of GWAS signal using GRIN identify additional genes contributing to suicidal behavior.” Communications Biology, 7(1), 1360. https://doi.org/10.1038/s42003-024-06943-7
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Multi‑omic network analysis identifies dysregulated neurobiological pathways in opioid addiction
Published in Biological Psychiatry, 2024
Biological Psychiatry paper identifying neurobiological pathways related to opioid addiction via multi-omic network analysis.
Recommended citation: Sullivan, K. A., Kainer, D., Lane, M., et al. (2024). “Multi‑omic network analysis identifies dysregulated neurobiological pathways in opioid addiction.” Biological Psychiatry.
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RWRtoolkit: multi‑omic network analysis using random walks on multiplex networks in any species
Published in GigaScience, 2025
GigaScience article introducing RWRtoolkit, a toolkit for random-walk restart analytics on multiplex biological networks.
Recommended citation: Kainer, D., Lane, M., Sullivan, K. A., et al. (2025). “RWRtoolkit: multi‑omic network analysis using random walks on multiplex networks in any species.” GigaScience.
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Population and adaptation history of 739 Thlaspi arvense natural accessions
Published in bioRxiv, 2025
Preprint describing the genomic and phenotypic analysis of 739 pennycress accessions to guide climate-resilient domestication efforts.
Recommended citation: Wu, X., Epstein, R., Esfahanian, M., Gautam, B., Griffiths, M., Perez, J., … Lane, M., et al. (2025). “Population and adaptation history of 739 Thlaspi arvense natural accessions.” bioRxiv. https://doi.org/10.1101/2025.03.21.644658
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Genome shuffling enables quantitative trait locus mapping in Bacillus subtilis
Published in bioRxiv, 2025
Preprint describing the use of iterative genome shuffling to enable bacterial QTL mapping in Bacillus subtilis, revealing genotype–phenotype associations through recombination-enabled linkage analysis.
Recommended citation: Vasileva, D. P., Chhetri, H. B., Hochanadel, L. H., Streich, J. C., Lagergren, J. H., Lane, M. J., et al. (2025). “Genome shuffling enables quantitative trait locus mapping in Bacillus subtilis.” bioRxiv. https://doi.org/10.1101/2025.07.14.664384
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Climate adaptation in Populus trichocarpa: key adaptive loci identified for stomata and leaf traits
Published in New Phytologist, 2025
This study identifies genetic and physiological traits underpinning drought adaptation in Populus trichocarpa, a key bioenergy crop. By examining over 1300 genotypes in a large-scale field experiment, the authors reveal that stomatal and leaf traits—especially a major locus on chromosome 10—are tightly linked to climatic origin and are likely to evolve under future arid conditions. Their findings provide crucial targets for breeding resilient trees to meet biofuel demands on marginal lands in a changing climate.
Recommended citation: Klein, M. C., Meng, Z., Bailey‐Bale, J., Milner, S., Shi, P., Muchero, W., Chen, J., Tschaplinski, T. J., Jacobson, D., Lagergren, J., Lane, M., O’Brien, C., Chhetri, H., Abeyratne, C. R., Shu, M., Freer‐Smith, P., Buckley, T. N., Magney, T. S., Monroe, J. G., … Taylor, G. (2025). Climate adaptation in Populus trichocarpa: key adaptive loci identified for stomata and leaf traits. New Phytologist. https://doi.org/10.1111/nph.70343
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talks
teaching
Introduction to Computing with C++
Undergraduate course, University of Missouri - St. Louis, Department of Computer Science, 2019
An introductory computer science course (CS1250) for students with no prior programming background. This course builds a foundation in computational thinking and procedural programming using C++.
Introduction to Web Programming: HTML, Javascript, and PHP
Undergraduate course, University of Missouri - St. Louis, Department of Computer Science, 2019
This course (CS3010) introduces students to the foundational technologies behind the web.
Advanced Web Programming with Javascript
Undergraduate course, University of Missouri - St. Louis, Department of Computer Science, 2020
Course material for CS4140, covering modern JavaScript frameworks and backend development techniques.
Introduction to Object Oriented Programming with Java
Undergraduate course, University of Missouri - St. Louis, Department of Computer Science, 2020
Course material for CS2261, covering Java fundamentals, objects, interfaces, abstract classes, and object-oriented programming principles.
Linux Systems Programming: Bash && C
Undergraduate course, University of Missouri - St. Louis, Department of Computer Science, 2020
This course (CS2700) is an introduction to Linux systems and system-level programming in C and Bash.